(1) Field of Invention
The present invention relates to a system for visual object recognition and, more particularly, to a system for visual object recognition which utilizes heterogeneous classifier cascades.
(2) Description of Related Art
Visual object recognition is one of the most critical tasks for video and image analysis applications. Typical approaches to visual object recognition involve using a chain of classifiers to quickly reject most non-object regions with a minimum of computation, and process the more object-like regions with high accuracy using computationally expensive features. In addition, most existing approaches use homogeneous features consisting of the same feature type, such as Haar or Gabor wavelets. Related prior art utilizes classifier cascades built from a fixed set of features for detecting objects in images. All stages in the cascade are similar and trained using the same training data or sometimes a supplemental bootstrap dataset. Stages increase in complexity and accuracy as the data moves through the cascade. In current approaches, the stages in the cascade do not concentrate on specific tasks, such as maximizing the detection rate or minimizing the false alarm rate.
Thus, a continuing need exists for a system for visual object recognition that significantly improves the detection rate and reduces the false alarm rate by dedicating certain stages in a classifier cascade for specific tasks and also by using a heterogeneous and complementary collection of features for more robust stages.